Advances in available computer power have enabled the development of computer vision systems through machine learning systems with feature detection engines employing neural networks. In computer vision applications, traditional neural networks generally mimic a biological eye by dividing an input image into individual receptive fields (e.g., grid cells of image pixels) that are then processed by respective neurons or processing nodes of the neural network. However, the segmentation of the input image can also create technical challenges for image recognition when an object or feature spans multiple receptive fields or grid cells because the neural network would have to reconcile multiple predictions of the same object.